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Barbara Simons proves how easy it is to hack elections—and how it can be stopped

College of Engineering Distinguished Alumna Barbara Simons (Ph.D. '81) is the subject of an article in the Dail Kos titled "Computer scientist Barbara Simons proves how easy it is to hack elections—and how it can be stopped." Simons, who runs Verified Voting, has been a longtime advocate for bringing paper ballots back to all states and exposing the perils of electronic paperless ballots. Last summer, she ran an experiment at the Def Con Hacker Conference in Las Vegas in which she secured 4 voting machines and had two teams of hackers successfully compromise them. “Anything that’s happening in here, you can be sure [it’s something] that those intent on undermining the integrity of our election systems have already done,” she said. Simons will be a keynote speaker at the WiCSE 40th Reunion on Saturday.

Michael Jordan named Plenary Lecturer at the 2018 International Congress of Mathematicians (ICM)

Prof. Michael Jordan has been named a Plenary Lecturer at the 2018 International Congress of Mathematicians (ICM), which will take place in Rio de Janeiro, Brazil, in August. ICM is considered the world’s premier forum for presenting and discussing new mathematical discoveries. Plenary speakers are invited from around the world to present one-hour lectures which are held without other parallel activities--an honor that has been bestowed on only a small handful of computer scientists over the 121 year history of the ICM.

Ashokavardhanan, Jung, and McConnell named KPCB Engineering Fellows

Undergraduate students Ganeshkumar Ashokavardhanan (EECS + Business M.E.T.), Naomi Jung (CS BA), and Louie McConnell (EECS + Business M.E.T.) have been selected to participate in the 2018 KPCB Engineering Fellows Program, named one of the top 5 internship programs by Vault. Over the course of a summer, KPCB Engineering Fellows join portfolio companies, where they develop their technical skills and are each mentored by an executive within the company. It offers students an opportunity to gain significant work experience at Silicon Valley startups, collaborating on unique and challenging technical problems.

AI training may leak secrets to canny thieves

A paper released on arXiv last week by a team of researchers including Prof. Dawn Song and Ph.D. student Nicholas Carlini (B.A. CS/Math '13), reveals just how vulnerable deep learning is to information leakage. The researchers labelled the problem “unintended memorization” and explained it happens if miscreants can access to the model’s code and apply a variety of search algorithms. That's not an unrealistic scenario considering the code for many models are available online, and it means that text messages, location histories, emails or medical data can be leaked. The team doesn't “really know why neural networks memorize these secrets right now, ” Carlini says. “At least in part, it is a direct response to the fact that we train neural networks by repeatedly showing them the same training inputs over and over and asking them to remember these facts." The best way to avoid all problems is to never feed secrets as training data. But if it’s unavoidable then developers will have to apply differentially private learning mechanisms, to bolster security, Carlini concluded.

How Flight Simulation Tech Can Help Turn Robots Into Surgeons

Robotics researchers from Berkeley's AUTOLab, led by IEOR and EECS professor Ken Goldberg, have built a heaving robotic platform — mimicking the motion of a breathing, heart-beating human patient — to help develop algorithms that robotic surgical assistants can use to guide their cutting. This research is the subject of an article in Wired magazine titled "How Flight Simulation Tech Can Help Turn Robots Into Surgeons." During surgery, when the chest heaves or blood pumps, the surgeon has to compensate for that movement. The researchers took the data from watching the surgeon's movements and developed algorithms that could mimic his strategy for cutting along a line. This new robot, which is a kind of a Stewart platform, mimics that movement. Stewart platforms are normally hefty pneumatic devices that power things like immersive flight simulators. But for this study, the researchers took the concept and shrunk it down to a 6-inch-wide device, opting for servo motors instead of pneumatic power. The machine costs just $250.

Retraining the brain’s vision center to take action

Neuroscience researchers, including Prof. Jose Carmena, have demonstrated the astounding flexibility of the brain by training neurons that normally process input from the eyes to develop new skills, in this case, to control a computer-generated tone. Carmena, the senior author of a paper about the development that appeared in the journal Neuron, explains that “to gain a reward, the rats learned to produce arbitrary patterns of neural activity unrelated to visual input in order to control a BMI, highlighting the power of neuroplasticity and the flexibility of the brain.” “These findings suggest that the striatum has a broader role in shaping cortical activity based on ongoing experience and behavioral outcomes than previously acknowledged, and have wide implications for the neuroscience of thought and action and brain-machine interfaces,” said Carmena.

RISELab's AI research wins $10M NSF award

The RISELab, led by Prof. Ion Stoica, has received an Expeditions in Computing award from the National Science Foundation (NSF), providing $10 million in funding over five years to enable game-changing advances in real-time decision making technologies. The award is presented to research teams pursuing large-scale, far-reaching and potentially transformative research in computer and information science and engineering. RISELab’s award will be used to develop technology for an era in which AI systems will make decisions that will play an increasingly central role in people’s lives in areas such as healthcare, transportation and business.

Ling-Qi Yan helps to improve computer rendering of animal fur

CS graduate student Ling-Qi Yan (advisors: Ravi Ramamoorthi/Ren Ng) and researchers at U.C. San Diego are the subject of an article in TechXplore titled "Scientists improve computer rendering of animal fur." He is part of a team that developed a method for dramatically improving the way computers simulate fur, and more specifically, the way light bounces within an animal's pelt. The researchers are using a neural network to apply the properties of a concept called subsurface scattering to quickly approximate how light bounces around fur fibers. The neural network only needs to be trained with one scene before it can apply subsurface scattering to all the different scenes with which it is presented. This results in simulations running 10 times faster than current state of the art. "We are converting the properties of subsurface scattering to fur fibers," said Yan. "There is no explicit physical or mathematical way to make this conversion. So we needed to use a neural network to connect these two different worlds." The researchers recently presented their findings at the SIGGRAPH Asia conference in Thailand.

Dan Wallach to testify about election security and voting machines in Texas

EECS alumnus Dan Wallach (B.S. '93) will testify before the Texas Senate Select Committee on Election Security at a hearing about recent election irregularities in Texas, a review of voting security protocols and the responsibilities and duties of members of the Electoral College. Specifically, the hearing will examine the use of electronic voting machines and paper ballots, voting fraud and disenfranchisement occurring inside nursing homes and assisted living facilities, outside interference and manipulation of elections, and the voting requirements of presidential electors. Wallach is widely regarded as an expert on voting machine security. He is currently an EECS professor at Rice University and a scholar at Rice's Baker Institute for Public Policy.

Security for data analytics – gaining a grip on the two-edged sword

Prof. Dawn Song and graduate student Noah Johnson are taking a new approach to enable organizations to follow tight data security and privacy policies while enabling flexible data analysis, as well as machine learning for analysts. Working with Uber, they tested their system using a dataset of 8 million queries written by the company’s data analysts. The system is currently being integrated into Uber’s internal data analytics platform. With help from the Signatures Innovation Fellows program, they are advancing the system to provide the same level of security and flexibility for a broad range of data analysis and machine learning, whether needed in basic and medical research or business analytics.